38,849 research outputs found
Pseudo Mask Augmented Object Detection
In this work, we present a novel and effective framework to facilitate object
detection with the instance-level segmentation information that is only
supervised by bounding box annotation. Starting from the joint object detection
and instance segmentation network, we propose to recursively estimate the
pseudo ground-truth object masks from the instance-level object segmentation
network training, and then enhance the detection network with top-down
segmentation feedbacks. The pseudo ground truth mask and network parameters are
optimized alternatively to mutually benefit each other. To obtain the promising
pseudo masks in each iteration, we embed a graphical inference that
incorporates the low-level image appearance consistency and the bounding box
annotations to refine the segmentation masks predicted by the segmentation
network. Our approach progressively improves the object detection performance
by incorporating the detailed pixel-wise information learned from the
weakly-supervised segmentation network. Extensive evaluation on the detection
task in PASCAL VOC 2007 and 2012 [12] verifies that the proposed approach is
effective
Efficient Semidefinite Branch-and-Cut for MAP-MRF Inference
We propose a Branch-and-Cut (B&C) method for solving general MAP-MRF
inference problems. The core of our method is a very efficient bounding
procedure, which combines scalable semidefinite programming (SDP) and a
cutting-plane method for seeking violated constraints. In order to further
speed up the computation, several strategies have been exploited, including
model reduction, warm start and removal of inactive constraints.
We analyze the performance of the proposed method under different settings,
and demonstrate that our method either outperforms or performs on par with
state-of-the-art approaches. Especially when the connectivities are dense or
when the relative magnitudes of the unary costs are low, we achieve the best
reported results. Experiments show that the proposed algorithm achieves better
approximation than the state-of-the-art methods within a variety of time
budgets on challenging non-submodular MAP-MRF inference problems.Comment: 21 page
Limitations of semidefinite programs for separable states and entangled games
Semidefinite programs (SDPs) are a framework for exact or approximate
optimization that have widespread application in quantum information theory. We
introduce a new method for using reductions to construct integrality gaps for
SDPs. These are based on new limitations on the sum-of-squares (SoS) hierarchy
in approximating two particularly important sets in quantum information theory,
where previously no -round integrality gaps were known: the set of
separable (i.e. unentangled) states, or equivalently, the
norm of a matrix, and the set of quantum correlations; i.e. conditional
probability distributions achievable with local measurements on a shared
entangled state. In both cases no-go theorems were previously known based on
computational assumptions such as the Exponential Time Hypothesis (ETH) which
asserts that 3-SAT requires exponential time to solve. Our unconditional
results achieve the same parameters as all of these previous results (for
separable states) or as some of the previous results (for quantum
correlations). In some cases we can make use of the framework of
Lee-Raghavendra-Steurer (LRS) to establish integrality gaps for any SDP, not
only the SoS hierarchy. Our hardness result on separable states also yields a
dimension lower bound of approximate disentanglers, answering a question of
Watrous and Aaronson et al. These results can be viewed as limitations on the
monogamy principle, the PPT test, the ability of Tsirelson-type bounds to
restrict quantum correlations, as well as the SDP hierarchies of
Doherty-Parrilo-Spedalieri, Navascues-Pironio-Acin and Berta-Fawzi-Scholz.Comment: 47 pages. v2. small changes, fixes and clarifications. published
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Natural evolution strategies and variational Monte Carlo
A notion of quantum natural evolution strategies is introduced, which
provides a geometric synthesis of a number of known quantum/classical
algorithms for performing classical black-box optimization. Recent work of
Gomes et al. [2019] on heuristic combinatorial optimization using neural
quantum states is pedagogically reviewed in this context, emphasizing the
connection with natural evolution strategies. The algorithmic framework is
illustrated for approximate combinatorial optimization problems, and a
systematic strategy is found for improving the approximation ratios. In
particular it is found that natural evolution strategies can achieve
approximation ratios competitive with widely used heuristic algorithms for
Max-Cut, at the expense of increased computation time
Conformal phased array with beam forming for airborne satellite communication
For enhanced communication on board of aircraft novel antenna systems with broadband satellite-based capabilities are required. The installation of such systems on board of aircraft requires the development of a very low-profile aircraft antenna, which can point to satellites anywhere in the upper hemisphere. To this end, phased array antennas which are conformal to the aircraft fuselage are attractive. In this paper two key aspects of conformal phased array antenna arrays are addressed: the development of a broadband Ku-band antenna and the beam synthesis for conformal array antennas. The antenna elements of the conformal array are stacked patch antennas with dual linear polarization which have sufficient bandwidth. For beam forming synthesis a method based on a truncated Singular Value Decomposition is proposed
On Weighted Low-Rank Approximation
Our main interest is the low-rank approximation of a matrix in R^m.n under a
weighted Frobenius norm. This norm associates a weight to each of the (m x n)
matrix entries. We conjecture that the number of approximations is at most
min(m, n).
We also investigate how the approximations depend on the weight-values.Comment: 13 page
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